EAI Endorsed Transactions on e-Learning

Key performance indicators-based monitoring, measuring progress and ranking of Aspirational Districts

2022-12-31T12:16:57+00:00

The Aspirational District Program (ADP) is an effort to transform those districts which are lacking in development. It focuses on each district's strengths, identifies areas for quick development, and tracks advancement by rating districts regularly. By competing with and learning from others in the spirit of competitive & cooperative federalism, districts are pushed and encouraged to first catch up with the best district within their state, and then aim to become one of the best in the nation. The government's "Sabka Saath Sabka Vikas aur Sabka Vishwas" initiative aims to improve residents' quality of life while promoting inclusive growth. The ADP fundamentally aims to localise the Sustainable Development Goals, resulting in national advancement. In this paper we have described the delta rankings, Key performance Indicators (KPI) and the effects on development of aspirational districts. The delta ranking of the Aspirational Districts combines the innovative use of data with pragmatic administration, keeping the district at the locus of inclusive development.

Review of Graph Neural Networks for Medical Image

2023-11-10T10:49:27+00:00

As deep learning continues to evolve, more and more applications are generating data from non-Euclidean domains and representing them as graphs with complex relationships and interdependencies between objects. This poses a significant challenge to deep learning algorithms. Because, due to the uniqueness of graphs, applying deep learning to ubiquitous graph data is not an easy task. To solve the problem in non-Euclidean domains, graph Neural Networks (GNNs) have emerged. A graph neural network (GNN) is a neural model that captures dependencies between graphs by passing messages between graph nodes. With the continuous development of medical image technology, medical image diagnosis plays a crucial role in clinical practice. However, in practice, medical images are often affected by noise, artifacts, and other interfering factors, which may lead to inaccurate and unstable diagnostic results. Therefore, image-denoising techniques become especially critical in medical image processing. Therefore, researchers have proposed innovative methods based on graph neural networks for effective noise removal, preserving the key features of the image and improving the quality and usability of medical images. This paper reviews the research progress of graph neural networks in the field of medical image denoising. It also summarises the problems and challenges of current research and looks at the future direction of medical image-denoising research.

Review of AlexNet for Medical Image Classification

2023-11-15T02:18:56+00:00

In recent years, the rapid development of deep learning has led to a wide range of applications in the field of medical image classification. The variants of neural network models with ever-increasing performance share some commonalities: to try to mitigate overfitting, improve generalization, avoid gradient vanishing and exploding, etc. AlexNet first utilizes the dropout technique to mitigate overfitting and the ReLU activation function to avoid gradient vanishing. Therefore, we focus our discussion on AlexNet, which has contributed greatly to the development of CNNs in 2012. After reviewing over 40 papers, including journal papers and conference papers, we give a narrative on the technical details, advantages, and application areas of AlexNet.

Deep learning-based lung nodule detection: a review

2024-01-05T13:04:10+00:00

CT scan acquisition is fast and cost-effective and has become the main lung imaging tool. However, the increase in large numbers of CT scans has placed a heavy burden on radiologists; therefore, automated lung nodule detection techniques are needed to reduce the workload of radiologists and computer-aided detection systems are proposed for further accurate diagnosis of the condition. This review provides a comprehensive overview of recent automated lung nodule detection techniques and challenges, etc., as well as a detailed overview and discussion of current research gaps, future developments, and research trends. Relevant articles published in databases such as IEEE Xplore, Science Direct, PubMed, and Web of Science cover research algorithms published from 2014 to 2023, mainly discussing deep learning-based techniques. The schemes presented in these articles, the databases used, the experimental results, and the performance of the algorithms are compared and discussed. This work aims to introduce researchers and readers to the latest techniques and their advances in the detection of lung nodules in the last decade, which will help researchers and radiologists to further understand the latest techniques in this field.

A Review of Deep Learning Approaches for Early Diagnosis of Alzheimer's Disease

2024-01-07T01:25:49+00:00

Alzheimer's disease (AD), one of the major neurodegenerative diseases, has become the most common cause of dementia problems. Up to now, there is a lack of effective targeted therapeutic drugs and effective treatment modalities to stop the progression of the disease. With the continuous development of computer technology, the use of computer-aided diagnostic technology tools for AD early classification studies will provide clinicians with important assistance. Deep learning-based Alzheimer's disease (AD) imaging classification has become a current research hotspot. In this paper, we first describe the commonly used publicly available datasets in the AD imaging classification task; then introduce the commonly used deep learning classification models for AD diagnosis; secondly, we compare the studies that target different biomarkers of the subjects and the use of unimodal or a combination of different modalities for the early classification of AD; and finally, The challenges of AD classification are summarized and future research directions are proposed.

A review of research and development of semi-supervised learning strategies for medical image processing

2024-01-11T12:48:27+00:00

Accurate and robust segmentation of organs or lesions from medical images plays a vital role in many clinical applications such as diagnosis and treatment planning. With the massive increase in labeled data, deep learning has achieved great success in image segmentation. However, for medical images, the acquisition of labeled data is usually expensive because generating accurate annotations requires expertise and time, especially in 3D images. To reduce the cost of labeling, many approaches have been proposed in recent years to develop a high-performance medical image segmentation model to reduce the labeling data. For example, combining user interaction with deep neural networks to interactively perform image segmentation can reduce the labeling effort. Self-supervised learning methods utilize unlabeled data to train the model in a supervised manner, learn the basics and then perform knowledge transfer. Semi-supervised learning frameworks learn directly from a limited amount of labeled data and a large amount of unlabeled data to get high quality segmentation results. Weakly supervised learning approaches learn image segmentation from borders, graffiti, or image-level labels instead of using pixel-level labeling, which reduces the burden of labeling. However, the performance of weakly supervised learning and self-supervised learning is still limited on medical image segmentation tasks, especially on 3D medical images. In addition to this, a small amount of labeled data and a large amount of unlabeled data are more in line with actual clinical scenarios. Therefore, semi-supervised learning strategies become very important in the field of medical image processing.

The Analysis of How Artificial Intelligence Has an Effect on Teachers and The Education System

2023-06-27T18:23:33+00:00

Artificial intelligence has established a presence in every aspect of human activity, further demonstrating its preeminence over humans with each passing day. Artificial Intelligence (AI) has demonstrated its prowess in a variety of industries, including but not limited to healthcare, robotics, eCommerce, finance, navigation, education (E), and many more. This study will investigate the effects that artificial intelligence has had on the educational system, with a particular emphasis on how AI has changed the responsibilities that instructors play in the classroom. This study will also focus on examining whether or not the presence of AI in the educational system will take over the responsibilities that are traditionally filled by teachers. The incorporation of artificial intelligence into educational and instructional systems has resulted in significant improvements in terms of the efficiency, precision, and variety of pedagogical approaches. It has also been demonstrated in a large number of studies that the most important factor in the achievement of AI domination in educational institutions is the role played by educators.

The E-learning for Alzheimer's Disease

2023-10-28T15:20:59+00:00

With the increase of the aging population, the incidence rate of Alzheimer's disease (AD) is also rising. Faced with this challenge, e-learning, as an innovative educational method, has shown great potential in the care and management of Alzheimer's disease patients. This article reviews the application progress of E-learning in Alzheimer's disease. E-learning has revolutionized the field of education, providing learners with accessible and flexible learning opportunities. This paper provides an overview of various aspects of e-learning, including virtual classrooms, mobile learning, blended learning, Massive Open Online Courses (MOOCs), webinars, and the challenges associated with implementing e-learning.

The background section explores the evolution of e-learning, highlighting its rise in popularity and the advancements in technology that have facilitated its growth. Virtual classrooms for adult learners are discussed, showcasing how these online platforms facilitate interactive and collaborative learning experiences. Mobile learning for adult learners is examined, emphasizing the convenience and accessibility provided by mobile devices in delivering educational content.

Blended learning is another approach explored in this paper, which combines traditional face-to-face instruction with online components, offering a balanced learning experience. The benefits and challenges of implementing MOOCs, which provide free and open access to educational resources from top institutions, are also examined. Additionally, webinars are discussed as a popular method for delivering live online presentations and workshops to adult learners.

Finally, the paper addresses the challenges of E-learning, including technological barriers, lack of personal interaction, and the need for self-discipline and motivation. Strategies for overcoming these challenges are suggested, such as providing technical support and fostering online community engagement.

Overall, this paper provides valuable insights into the background and various approaches to E-learning, as well as the challenges encountered in its implementation. Understanding these aspects will help educators and institutions effectively harness the potential of E-learning to enhance adult education.

Discrete Wavelet Analysis: A Mighty Approach for Image Segmentation

2023-10-30T05:15:06+00:00

This paper explores the application of Discrete Wavelet Analysis, a mathematical and signal processing technique, in the context of image segmentation, which provide a pixel-level or region-level decomposition of the image, enabling the extraction of relevant information for subsequent analysis and interpretation. Introducing the basic image segmentation techniques and the DWA, this paper discovers that DWA has found widespread application in fields such as signal processing, image analysis, and data compression. Compared with Fourier Transform, DWA is more suitable for image segmentation, having unique advantages and characteristics. Among the procedures of image segmentation, the most important point is feature selection, which determine the criteria for distinguishing different regions within the image. Despite DWA has many advantages, this technology also owns many challenges and limitations, which may be solved by lasting academic research to refine and extend Discrete Wavelet Analysis methodologies for image segmentation. In short, this research highlights the promise of Discrete Wavelet Analysis, emphasizing the use of high- quality image processing.

Microlearning helps Alzheimer’s Disease Patients

2023-11-07T11:14:26+00:00

Alzheimer's disease is one of the most common diseases in older adults, and as the disease progresses, the need for daily care increases. Caregivers of Alzheimer's Disease patients face a variety of stresses and work pressures. Receiving professional and continuous training is one of the effective ways to improve their skills and competencies. A new approach to education is microlearning, where microeducational content is provided to learners. Microlearning as a pedagogical technique focuses on designing learning modules through micro-steps in a digital media environment. These activities can be integrated into learners' daily lives and tasks. Unlike "traditional" e-learning methods, microlearning often favours technology delivered through push media, thus reducing the cognitive load on the learner. Microlearning educational methods have been shown to be effective and efficient in educating and delivering materials to caregivers of older adults with Alzheimer's disease. This paper begins with a brief introduction to microlearning. And it details the key features and benefits of microlearning. Microlearning offers potential benefits to Alzheimer's Disease patients and their caregivers with its concise and focused approach. Secondly, machine learning enhances the design and delivery of microlearning, helping to provide a more personalised and effective learning experience. Machine learning plays a role in the design of microlearning. To conclude, microlearning offers a promising avenue of support and care for Alzheimer's Disease patients. Microlearning also provides a valuable resource for carers and healthcare professionals to gain the knowledge and skills needed to provide effective care.

Use MOOC to learn image denoising techniques

2023-11-15T14:54:19+00:00

This article focuses on using MOOCs to learn image denoising techniques. It begins with an introduction to the concept of MOOCs - these innovative online learning platforms that offer a wide range of courses across disciplines, providing convenient and affordable learning opportunities for a global audience. It then explains the characteristics of MOOC's wide coverage, high flexibility, and different from traditional education models. It then introduces the advantages of MOOCs: accessibility and inclusiveness (open to anyone with an Internet connection), cost-effectiveness (a cost-effective alternative, many courses available for free), flexibility and self-paced learning (the ability to learn at your own pace), a diverse curriculum and global expertise. Then the concept of image denoising is introduced - image denoising is a basic process of digital image processing, and the common denoising methods are described: filter method and the applicable range of various filters, the advantages and disadvantages of wavelet change, the advantages of deep learning method and the principle of non-local mean denoising technology. It then describes how MOOCs can help learn image denoising: integrating course content, getting expert guidance, hands-on exercises and projects, and community and peer communication. In addition, it introduces the challenges encountered by MOOCs: high dropout rate, quality and credibility of MOOCs, lack of interaction and humanization in traditional classrooms, accessibility. The relationship between E-learning and MOOC is also introduced – E-learning and MOOC play complementary roles in modern education. MOOC provide a structured, flexible, cost-effective environment and a transformative educational experience for learning about biological image denoising.

Mobile Learning for COVID-19 Prevention

2023-11-17T08:10:29+00:00

In recent years, due to the explosion of COVID-19, people's expectation for accessing personalized learning resources anytime and anywhere has become stronger. The features of m-learning such as accessibility and personalization greatly satisfy people's needs and are therefore widely used. In this paper, a study was conducted to investigate and analyze how m-learning can help prevent COVID-19. The study shows that m-learning can help disseminate outbreak-related messages and provide people with personalized knowledge, so that it can enhance public health and community safety. While there are still many challenges, m-learning remains a valuable tool for preventing and mitigating the spread of COVID-19 globally, and provides a solid reference for deepening m-learning development in the future.

Controllable Privacy-Preserving Online Diagnosis with Outsourced SVM over Encrypted Medical Data

2023-11-18T10:18:22+00:00

With the widespread application of online diagnosis systems, users can upload their physical characteristics anytime and from anywhere to receive clinical diagnoses. However, for privacy and intellectual property considerations, users' physical characteristics, diagnosis results, and the medical diagnosis model should be protected. To achieve an efficient and secure online diagnosis, secure outsourcing and low burden become research objectives. However, few of the existing privacy-preserving schemes focus on the secure outsourcing of the training process, and few consider the supervision of the hospital for the online diagnosis process. By introducing a four-party architecture with two non-colluding servers, a hospital and users, in this paper, we propose a controllable privacy-preserving online diagnosis scheme (CPPOD) with outsourced SVM over encrypted medical data. Concretely, an integer vector homomorphic encryption is employed to protect medical data and user requests. In the encrypted domain, a series of collaborative protocols including data collection, sequence minimum optimization solver, SVM model building, and online diagnosis are constructed and take place between different participants, while no significant increase in computation on either the hospital or user side. CPPOD enables the hospital to delegate online diagnosis services to a cloud server while ensuring that its regulatory capabilities cannot be bypassed unauthorized. Security analysis and performance evaluation suggest that CPPOD performs well regarding security and efficiency.

A K-Anonymous Location Privacy-Preserving Scheme for Mobile Terminals

2023-11-24T03:13:24+00:00

Mobile terminals boost the prosperity of location-based service (LBS) which have already involved in every aspect of People's daily life and are increasingly used in various industries. Aimed at solving the security and efficiency problem in the existing location privacy protection schemes, a K-anonymity location privacy preservation scheme based on mobile terminal is proposed. Firstly, number of rational dummy locations is selected from the cloaking region, from which more favorable locations are further filtered according to location entropy, so a better anonymity effect can be achieved. Secondly, the secure and efficient m-out-of-n oblivious transfer protocol is adopted, which not only avoids the dependency on the trusted anonymity center in existing schemes to improve the efficiency, but also meets the requirements for querying multiple interest points at one time. Security analyses demonstrate that this scheme satisfies such security properties as anonymity, non-forgeability and resistance to replay attack, and simulation results show that this scheme has higher execution efficiency and privacy level, while is low in communications costs.

Efficient Course Recommendation using Deep Transformer based Ensembled Attention Model

2023-11-24T10:17:16+00:00

The exponential development of online learning resources has led to an information overload problem. Therefore, recommender systems play a crucial role in E-learning to provide learners with personalised course recommendations by automatically identifying their preferences. In addition, e-Learning platforms such as MOOCs and LMS have been criticised for their low course completion rates, and one of the primary reasons is that they do not provide personalised course recommendations for users with varying interests. Rapidly locating the courses that users are interested in on enormous e-Learning platforms can have a significant impact on the quality of learning and the dissemination of knowledge to the learner. This paper examines the most prevalent recommendation techniques utilised in E-learning. We examined how to apply Deep Transformer based Ensembled Attention Model (DTEAM) on e-Learning system in order to achieve personalized course recommendations. The proposed recommendation model uses BERT as its foundation integrated MLM and Transformers. Predicted course recommendations are more aligned with the interests of users. Our experimental results proved that traditional recommendation algorithms, such as collaborative filtering and item-based filtering are incapable of producing superior results. The consequence of the research can assist students in selecting courses according to their preferences and improve their learning caliber

Empowering Young Athletes: Elevating Anti-Doping Education with Virtual Reality

2023-12-04T10:48:40+00:00

In recent times, doping's prevalence in sports has gained substantial recognition, sparking a concerted effort from researchers, policymakers, and sports bodies to underscore the critical role of impactful anti-doping education initiatives. An exhaustive examination of current literature underscores a critical requirement for advanced educational interventions that can effectively combat the multifaceted challenges presented by doping across the spectrum of competitive and recreational athletes. In response to this exigency, this paper introduces an innovative paradigm to redefine anti-doping education through the fusion of virtual reality (VR) technology. This proposed approach seeks to leverage VR's immersive potential, offering dynamic and interactive learning experiences that authentically mirror the complexities surrounding doping decisions. By immersing athletes within lifelike scenarios, VR education aims to provide a nuanced understanding of the psychological and emotional facets associated with doping, all within a secure and controlled environment. However, while the potential of VR in anti-doping education is promising, it also necessitates addressing technical, ethical, and usability considerations, an aspect that this paper further explores.

Development and Perceived Usability Evaluation of a Mobile application for Notetaking

2023-12-04T13:08:23+00:00

INTRODUCTION: Notetaking is considered, by many educators, as one of the critical actions of learning. There are several note-taking methods and approaches. Based on these methods and approaches, various applications, whether mobile, desktop or -Web-based, were developed.

OBJECTIVES: In this paper, a novel note-taking application based on Cornell Technique, is presented. Its development process and user acceptance trend are exhibited and results for user evaluation based on user satisfaction are presented.

METHODS: For the software development process, Incremental Model was adopted. Requirement Analysis included, aside from examining principles and related note-taking structure of Cornell Technique, investigating (i) how to perform notetaking as an activity of learning, (ii) its product and (iii) relationship of notes for the purpose of storage. Models containing sub-activities, such as reviewing note have been identified and some were selectively adopted and related functions such as review alert (tickler) and collaboration on notetaking have been implemented. To the purpose of storage, a tree-based scheme called collection was modelled. User interfaces were first designed as mockups and click-through pro-totype using Adobe XD. The mobile application was implemented in Dart programming language. Google’s Firebase Service and Flutter Framework was adopted. The mobile application was compared with its equivalents in the Google Play Store and user statistics were investigated. To evaluate perceived usability, the System Usability Scale is adopted and applied to 14 university students conforming to determined persona.

RESULTS: The application has been published in Google Play Store for users to install for free on 18^th March 2022. As of 10^th September 2023, total number of downloads is 5K and the Cornell Note mobile app is currently installed on 1108 devices. For the last three-month period (from 11^th June to 10^th September 2023), the active users per month changed in an increasing trend from 450 to 589. The average engagement time on 11^th of April 2023 was 28 minutes 00 seconds. As the number of monthly active users increased, the average engagement time measured on 10^th September 2023 decreased to 23 minutes 31 seconds. However, engagement rates measured were 76.91% and 77.19%, respectively. The mean SUS score was found to be equal to 79.5.

CONCLUSION: The user statistics and comparison with equivalent mobile applications reveal that Cornell Note has potential to grow as a mobile application for notetaking since it has a good perceived usability, however, there is room for improvement. Considering any extra marketing effort was not spent for the application such as application store optimization, the statistics are another evidence for user appeal and acceptance. However, it is important to add new functionality without complicating the user experience so that user appeal and acceptance boosts.

ChatGpt impact on Student Educational Performance: a conceptual analysis

2023-12-11T19:25:02+00:00

This research examines ChatGPT, a sophisticated AI language model created by OpenAI, and its effect on learning outcomes. The research explores the impact of implementing ChatGPT into different learning contexts with a mixed methods approach, combining quantitative data from educational performance measures with qualitative findings gathered from student surveys. Based on these early results, it seems that ChatGPT helps students learn more because it simplifies down challenging subject matter, provides immediate feedback, and encourages collaborative learning. The technology also looks to play a major role in boosting educational accessibility, closing the knowledge gap for student in underserved areas or with restricted access to teachers. The study also highlights the significance of using ChatGPT to supplement more conventional teaching strategies rather than as a replacement for them.

Ethic wars: student and educator attitudes in the context of ChatGPT

2024-01-21T17:21:44+00:00

Technologists and educators have been both fascinated and frightened since the publication of ChatGPT. ChatGPT has both supporters and detractors, but it is informative for individuals in the education community to look at the educational research on AI in education in order to gain understanding and establish objective judgments about the importance of ChatGPT in education. In this paper, we first present the journey of OpenAI GPT models, then give the implications of ChatGPT for education. Then, we list works for detection ChatGPT based texts and other precautions. Finally, an example of an exam with ChatGPT answers is given.

Blended Learning for Machine Learning-based Image Classification

2023-11-30T07:36:14+00:00

The paper commences with an introduction to blended learning, an educational approach that amalgamates traditional face-to-face instruction with online learning, aiming to capitalize on the advantages of conventional classroom instruction and digital resources in order to enhance the overall learning experience. The incorporation of diverse technologies facilitates a personalized learning experience that caters to the needs and learning styles of individual students. Image classification entails training machine learning models to categorize or label images into predetermined classes or categories, empowering machines to recognize and comprehend crucial components of visual information, emulating humans' classification of objects in the real world. The crux of image classification relies on extracting meaningful features from images and distinguishing different categories by associating specific features with distinct classes through iterative optimization learning. Machine learning significantly aids image classification by endowing automated systems with the capability to discern patterns, features, and distinctions within datasets, ultimately achieving accurate image classification. The integration of hybrid learning methods can augment the training process for machine learning models used in image classification by providing a flexible and adaptive learning environment.